National Scale Land Cover Classification Using the Semiautomatic High-Quality Reference Sample Generation (HRSG) Method and an Adaptive Supervised Classification Scheme

Abstract

The advent of new high-performance cloud computing platforms [e.g., Google Earth Engine (GEE)] and freely available satellite data provides a great opportunity for land cover (LC) mapping over large-scale areas. However, the shortage of reliable and sufficient reference samples still hinders large-scale LC classification. Here, selecting Turkey as the case study, we presented a semiautomatic high-quality reference sample generation (HRSG) method using the publicly available scientific LC products and the linear spectral unmixing analysis to generate high-quality ground samples for the years 1995 and 2020 within the GEE platform. Furthermore, we developed an adaptive random forest classification scheme based on Köppen–Geiger climate zone classification system. Our rationale was related to the fact that large-scale study areas often contain multiple climate zones where the spectral signature of the same LC class may vary within different climate zones that can lead to a poor LC classification accuracy. To have a robust assessment, the generated LC maps were evaluated against independent test datasets. In regard to the proposed sample generation method, it was observed that HRSG can generate high-quality samples independent of the characteristics of scientific LC products. The high overall accuracy of 92% for 2020 and 90% for 1995 and satisfactory results for producer's accuracy (ranging between 83.4% and 99.3%) and user's accuracy (ranging between 86.1% and 99.7%) of nine LC classes demonstrated the effectiveness of the proposed framework. The presented methodologies can be incorporated into future studies related to large-scale LC mapping and LC change monitoring studies.

Keywords

• Global land cover (LC) products
• Google Earth Engine (GEE)
• LC classification
• linear spectral unmixing (LSU)
• reference samples